利用人工智能来检测电子健康记录中的污名化语言,以推进健康公平
Teenu Xavier1, Qinglin Pei2, Jane M Carrington2
1College of Nursing, University of Florida, Gainesville, FL; School of Nursing, College of Public Health, George Mason University, Fairfax, VA.
Nursing outlook
|August 5, 2025
概括
人工智能准确地检测到电子健康记录 (EHR) 中的污名化语言,发现它与更长的住院时间有关,并且不成比例地影响老年患者和政府保险患者.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 健康差距 研究 研究 研究 研究
背景情况:
- 电子健康记录 (EHR) 中的污名化语言存在重大风险.
- 它可能会对患者与提供者关系,临床决策和患者的结果产生负面影响.
- 解决这种语言对于公平的医疗保健服务至关重要.
研究的目的:
- 开发和验证一种方法,用于识别在电子健康文件中的污名化语言.
- 调查污名化语言与患者结果之间的关联.
- 了解与污名化语言存在相关的人口结构模式.
主要方法:
- 追溯分析了500名患者的75,654份临床笔记.
- 利用机器学习模型来检测污名化的语言.
- 专注于在学术医疗中心的医院获得疾病的患者.
主要成果:
- 机器学习模型在识别污名化的语言方面取得了很高的准确性 (F1得分:0.95).
- 污名化的语言与住院时间的延长有显著的关联.
- 老年患者和那些有政府保险的人更有可能在他们的记录中使用污名化语言.
结论:
- 人工智能支持的语言建模有效地识别了EHR中的污名化语言.
- 这种方法可以揭示护理模式,并识别有风险的患者.
- 缓解耻辱化的语言对于改善患者护理和减少差异至关重要.
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